Optimal and lead-in adaptive allocation for binary outcomes: A comparison of Bayesian methods |
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Authors: | Roy T Sabo Ghalib Bello |
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Institution: | Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA |
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Abstract: | We compare posterior and predictive estimators and probabilities in response-adaptive randomization designs for two- and three-group clinical trials with binary outcomes. Adaptation based upon posterior estimates are discussed, as are two predictive probability algorithms: one using the traditional definition, the other using a skeptical distribution. Optimal and natural lead-in designs are covered. Simulation studies show that efficacy comparisons lead to more adaptation than center comparisons, though at some power loss, skeptically predictive efficacy comparisons and natural lead-in approaches lead to less adaptation but offer reduced allocation variability. Though nuanced, these results help clarify the power-adaptation trade-off in adaptive randomization. |
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Keywords: | Adaptive randomization Bayesian methods Clinical trials Predictive probability |
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